Prestack Seismic Data Inversion for Shale Gas Reservoir Characterization in China; #41829 (2016)

Gang Yu, Yusheng Zhang, Uwe Strecker, Maggie Smith

Search and Discovery.com

... connected to well information through well-tie and wavelet extraction. Well data is also used in the low frequency model along with interpreted horizons...

Novel application of machine learning assisted fault interpretation to delineate earthquake risk from saltwater disposal in the Midland Basin

Niven Shumaker, Mohammed Afia

International Meeting for Applied Geoscience and Energy (IMAGE)

... seismic survey using a 3D convolutional neural for edge pixels in a 2D data array. Lines that pass through network (Abubakar et al. 2022). Fault points...

Introduction to Deep Learning: Part I

Hongbo Zhou, Lasse Amundsen, Martin Landrø

GEO ExPro Magazine

... that showed that computers could perform tasks once thought to be solely the domain of human capability. However, lack of computer power soon stopped...

Abstract: Utilizing Seismic Attributes for Machine Assisted Fault Detection and Extraction; #91204 (2023)

Muhammad Khan, Yasir Bashir, Saleh Dossary, Syed Ali

Search and Discovery.com

... labelled data as transfer learning to update the foundation Convolutional Neural Network (CNN) model that was initially trained on synthetic data...

Technical Article: Finding Subtle Traps with Seismic: Interpretative Criteria Clarified

A. Easton Wren

Petroleum Exploration Society of Australia (PESA)

... to what the section should look like. Progressive understanding of the seismic method introduced the concept of the convolutional model: this found...

Methods of estimating wavelet stationarity, stabilizing non-stationarity, and evaluating its impact on inversion: A synthetic example using SEAM II Barrett unconventional model

Jesse Buckner, Michael Fry, Joe Zuech, Peter Harris, Bill Shea

International Meeting for Applied Geoscience and Energy (IMAGE)

... is simulated across a continuous 3D convolutional synthetic seismic volume, derived from the earth model of the SEAM II Barrett dataset. Multiple...

Using Machine Learning to Automate FDI Analysis

Reid Thompson, Lance Legel, Thomas Hanlon

Unconventional Resources Technology Conference (URTEC)

... is an automated stage detection model. The core of the stage detection model is a onedimensional deep convolutional U-net neural network with residual layers...

APPLICATION OF MACHINE LEARNING IN COORDINATION NUMBER ESTIMATION FOR RESERVOIR ROCK EXTRACTION

Dinanti Syafirani Zahra, Maharani Arisandy, Shafa Maura Fidela, Aldenia Alexandra, and Irwan Ary Dharmawan

Indonesian Petroleum Association

..., relying on experimental data or manual image analysis. This study explores a machine learning approach using a custom- developed Convolutional Neural...

ABSTRACT: Seismic Heterogeneity Cubes and Corresponding Equiprobable Simulations; #90013 (2003)

Matthias Imhof, William Kempner

Search and Discovery.com

... attributes. Instead, model statistics with only six parameters are fitted to the raw statistics. These six parameters include three orthogonal...

Abstract: A Transfer Learning Approach to Rock Property Estimation Workflows;

Ahmad Mustafa, Motaz Alfarraj, Ghassan Alregib

Search and Discovery.com

.... This results in vertical discontinuities in the computed property volumes using such a model, since it becomes sensitive to lateral changes...

Automated hyper-parameter optimization for deep learning framework to simulate boundary conditions for wave propagation

Harpreet Kaur, Sergey Fomel, Nam Pham

International Meeting for Applied Geoscience and Energy (IMAGE)

... of spurious reflections and the padded model used to simulate the unbounded domain (Figures1). We first define hyper-parameters for the deep learning...

Time-lapse seismic data shaping with transformer encoder neural networks

Jorge E. Monsegny, Daniel O. Trad, Don C. Lawton

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Alali et al. (2021) use convolutional neural networks to perform this filtering, while Alali et al. (2022) employ recurrent neural networks to shape...

Transfer Learning Applied to Seismic Images Classification

Search and Discovery.com

Perceptual quality-based model training under annotator label uncertainty

Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib

International Meeting for Applied Geoscience and Energy (IMAGE)

...Perceptual quality-based model training under annotator label uncertainty Chen Zhou, Mohit Prabhushankar, Ghassan AlRegib Perceptual quality-based...

Assessing properties of internal multiples for different geologies

Paul Ras, Mikhail Davydenko, Eric Verschuur

International Meeting for Applied Geoscience and Energy (IMAGE)

... analysis: multiple reflectivity power spectrum (left) and frequency-time (FT) plot (right), model 2. The VSP display with FWMod modeling in Figure 10...

Earthquake Detection and Focal Mechanism Calculation Using Artificial Intelligence

Shane Quimby, Yanwei Zhao, Jie Zhang, GeoTomo

Unconventional Resources Technology Conference (URTEC)

... network (FCN). FCNs are supervised deep learning networks based on convolutional layers, without being fully connected. This necessitates fewer model...

Imaging and fold comparison of mirror reverse time migration vs. interferometric imaging for VSP data

Liwei Cheng, James Simmons, Ali Tura

International Meeting for Applied Geoscience and Energy (IMAGE)

.... Mirror migration and interferometric imaging utilize free-surface multiples to extend the subsurface illumination. We design a 2-D synthetic model...

Using machine learning to interpret 3D airborne electromagnetic inversions

Eldad Haber, Jen Fohring, Mike McMillan, Justin Granek

Petroleum Exploration Society of Australia (PESA)

... types of regularization and constraints to the model, but another approach is to learn what underlying structures or boundaries these smooth...

Post Migration Processing of Seismic Data

Dashuki Mohd.

Geological Society of Malaysia (GSM)

... or multiples. The basis for deconvolution is the convolutional model (Robinson, 1984). In the convolutional model, a seismic trace is viewed...

Deep-learning application of salt geometry detection in deep water Brazil

Ruichao Ye, Anatoly Baumstein, Kirk A. Wagenvelt, Erik R. Neumann

International Meeting for Applied Geoscience and Energy (IMAGE)

... a novel workflow based on a deep convolutional neural network for automatically detecting salt geometry from a seismic image. By developing...

Reservoir Modeling With Deep Learning

Search and Discovery.com

Abstract: FaciesNet: Machine Learning Applications for Facies Classification in Well Logs;

Chayawan Jaikla, Pandu Devarakota, Neal Auchter, Mohamed Sidahmed, Irene Espejo

Search and Discovery.com

... information, facies stacking pattern, and geologic correlations, FaciesNet. Our proposed model incorporates decoding and encoding deep convolutional...

Assessing and processing three-dimensional photogrammetry, sedimentology, and geophysical data to build high-fidelity reservoir models based on carbonate outcrop analogues

Ahmad Ramdani, Pankaj Khanna, Gaurav Siddharth Gairola, Sherif Hanafy, and Volker Vahrenkamp

AAPG Bulletin

... tomogram inverted from the synthetic ray path in (C). (F) The 2-D zero-offset convolutional reflection model calculated using a 120-Hz Ricker wavelet...

Seismic data augmentation for automatic faults picking using deep learning

Nam Pham, Sergey Fomel

International Meeting for Applied Geoscience and Energy (IMAGE)

... these newly generated data for training a convolutional neural network for faults picking, as the training data will resemble the field test data...

2D isotropic and vertical transversely isotropic RTM using SEG Hess VTI Model

Richa Rastogi, Abhishek Srivastava, Monika Gawade, Nithu Mangalath, Laxmaiah Bathula, Bhushan Mahajan, Suhas Phadke

International Meeting for Applied Geoscience and Energy (IMAGE)

...2D isotropic and vertical transversely isotropic RTM using SEG Hess VTI Model Richa Rastogi, Abhishek Srivastava, Monika Gawade, Nithu Mangalath...